Academic literature on the topic 'Hesitant adaptive search'

Abstract The Chinese Communist Party (CCP) is expanding its organizational infrastructure in the private sector, revealing the dynamics of CCP-style institutional change. Party building follows a distinct version of adaptive governance. Hesitant to rely on innovative tools alone, organizers productively tinker with traditional and disparate elements. Grassroots Party organs, sanctified by their venerable history, are redeployed – initially for modest purposes that fall short of their original revolutionary potential. The Party's surge in private-sector firms was triggered by technocrats overhauling Leninist systems to reconnect to Party members; the search for a broader mission came later. To empower CCP organs in companies, organizers use tactical precedents ranging from incentives to negotiations around Party financing, and membership discipline. Combining tactics from different eras, overseas Party building deploys old organizational arrangements to new ends, whereas digitization gives time-worn procedures a second life. The inclination for institutional bricolage is a deeply rooted hallmark of innovation in Chinese statecraft.

Abstract Background Over the last years, vaccine hesitancy has been increasing globally, leading to a decrease in vaccination coverage in several Countries, mostly in Europe. Several systematic reviews were conducted to assess the determinants of hesitancy and of vaccination choices, but none of them focused on pregnant women, which would represent an ideal population to promote correct information on vaccines. Methods We conducted a systematic review of studies assessing knowledge and attitudes on paediatric vaccinations, vaccination choices and their determinants among pregnant women. We searched Medline, Scopus and Web of Science, adapting search terms to the different databases, following PRISMA Statement criteria. No time limits were set. Results A total of 5,309 records were retrieved. After duplicates removal and title/abstract selection, 15 full texts were included in the narrative synthesis. Most studies (14/15) were quantitative surveys, 2 focused only on specific vaccines (rotavirus and HPV), 4 studies were conducted in European countries (Germany and Italy). The main factors associated with vaccine hesitancy or refusal were the fear of side effects, in particular for the most recent vaccines, the perception of a greater importance of natural immunity and a low perception of utility of vaccines, the lack of trust in health professionals, having obtained information on vaccines not from a doctor, primiparity and a low level of education (for these two last factors, with inconsistent results between studies). Conclusions Available evidence shows that the perceived risks and importance of vaccinations and the information received during pregnancy can influence vaccination choices of future mothers, and that the determinants of hesitancy may vary in different contexts. Pregnant women represent a key population for the implementation of interventions aimed at increasing knowledge and acceptance of vaccinations, which should be based on an analysis of the specific context. Key messages The perceived risks and importance of vaccinations and the information received during pregnancy can influence vaccination choices of future mothers. Interventions aimed at increasing knowledge and acceptance of vaccinations should be based on an analysis of the specific context.

This thesis addresses aspects of stochastic algorithms for the solution of global optimisation problems. The bulk of the research investigates algorithm models of the adaptive search variety. Performances of stochastic and deterministic algorithms are also compared.

Chapter 2 defines pure adaptive search, the prototypical improving region search scheme from the literature. Analyses from the literature of the search duration of pure adaptive search in two specialised situations are recounted and interpreted. In each case pure adaptive search is shown to require an expected search time which varies only linearly with the dimension of the feasible region.

In Chapter 3 a generalisation of pure adaptive search is introduced under the name of hesitant adaptive search. This original algorithm retains the sample point generation mechanism of pure adaptive search, but allows for hesitation, in which an algorithm iteration passes without an improving sample being located. In this way hesitant adaptive search is intended to model more closely practically implementable algorithms. The analysis of the convergence of hesitant adaptive search is more thorough than the analyses already appearing in the literature, as it describes how hesitant adaptive search behaves when given more general objective functions than in previous studies. By specialising to the case of pure adaptive search we obtain a unification of the results appearing in those papers.

Chapter 4 is the most applied of the chapters in this thesis. Here hesitant adaptive search is specialised to describe the convergence behaviour of localisation search schemes. A localisation search scheme produces a bracket of the current improving region at each iteration. The results of Chapter 3 are applied to find necessary and sufficient conditions on the 'tightness' of the brackets to ensure that the dependence of the expected search duration on the dimension of the feasible region is linear, quadratic, cubic, and so forth.

Chapter 5 describes another original generalisation of pure adaptive search, known as fenestral adaptive search. This algorithm generates sample points from a region determined not merely by the previous sample, but by the previous w samples, where w is some prespecified positive integer. The expected search duration of fenestral adaptive search is greater than that of pure adaptive search, but still varies only linearly with the dimension of the feasible region. The sequence of objective function values obtained constitutes an interesting stochastic process, and Chapter 5 is devoted to understanding this process.

Chapter 6 presents a theoretical comparison of the search durations of deterministic and stochastic global optimisation algorithms, together with some discussion of the implications. It is shown that to any stochastic algorithm, there corresponds a deterministic algorithm which requires no more iterations on average, but we discuss why stochastic algorithms may still be more efficient than their deterministic counterparts in practice.